




"""simple docstring"""





import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available

def        _UpperCamelCase     (      UpperCamelCase=None				)      ->      Optional[int]:


			"""simple docstring"""







			if subparsers is not None:
						__UpperCAmelCase						:			Optional[Any]												=       subparsers.add_parser("env"				)
			else:
						__UpperCAmelCase						:			List[str]												=       argparse.ArgumentParser("Accelerate env command"				)

			parser.add_argument(
			    "--config_file"					,							default=UpperCamelCase					,							help="The config file to use for the default values in the launching script."				)

			if subparsers is not None:
						parser.set_defaults(func=UpperCamelCase				)
			return parser

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Any:


			"""simple docstring"""
			__UpperCAmelCase						:			Any												=       torch.__version__
			__UpperCAmelCase						:			int												=       torch.cuda.is_available()
			__UpperCAmelCase						:			Union[str, Any]												=       is_xpu_available()
			__UpperCAmelCase						:			Tuple												=       is_npu_available()

			__UpperCAmelCase						:			str												=       "Not found"
			# Get the default from the config file.
			if args.config_file is not None or os.path.isfile(UpperCamelCase				):
						__UpperCAmelCase						:			List[Any]												=       load_config_from_file(args.config_file				).to_dict()

			__UpperCAmelCase						:			Tuple												=       {
			    "`Accelerate` version": version,
			    "Platform": platform.platform(),
			    "Python version": platform.python_version(),
			    "Numpy version": np.__version__,
			    "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
			    "PyTorch XPU available": str(UpperCamelCase				),
			    "PyTorch NPU available": str(UpperCamelCase				),
			    "System RAM": f"{psutil.virtual_memory().total / 1024 ** 3:.2f} GB",
			}
			if pt_cuda_available:
						__UpperCAmelCase						:			int												=       torch.cuda.get_device_name()

			print("\nCopy-and-paste the text below in your GitHub issue\n"				)
			print("\n".join([f"- {prop}: {val}" for prop, val in info.items()]				)				)

			print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:"				)
			__UpperCAmelCase						:			Optional[int]												=       (
			    "\n".join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()]				)
			    if isinstance(UpperCamelCase					,							UpperCamelCase				)
			    else f"\t{accelerate_config}"
			)
			print(UpperCamelCase				)

			__UpperCAmelCase						:			List[Any]												=       accelerate_config

			return info

def        _UpperCamelCase     (      )      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			List[Any]												=       env_command_parser()
			__UpperCAmelCase						:			int												=       parser.parse_args()
			env_command(UpperCamelCase				)
			return 0


if __name__ == "__main__":
					raise SystemExit(main())








'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     						=				logging.get_logger(__name__)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     []
   for i in range(config.num_hidden_layers						):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")						)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ('cls_token', 'deit.embeddings.cls_token'),
           ('dist_token', 'deit.embeddings.distillation_token'),
           ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
           ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
           ('pos_embed', 'deit.embeddings.position_embeddings'),
       ]						)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ('norm.weight', 'layernorm.weight'),
              ('norm.bias', 'layernorm.bias'),
              ('pre_logits.fc.weight', 'pooler.dense.weight'),
              ('pre_logits.fc.bias', 'pooler.dense.bias'),
          ]						)

      # if just the base model, we should remove "deit" from all keys that start with "deit"
      A__       =     [(pair[0], pair[1][4:]) if pair[1].startswith('deit'						) else pair for pair in rename_keys]
   else:
      # layernorm + classification heads
      rename_keys.extend(
          [
              ('norm.weight', 'deit.layernorm.weight'),
              ('norm.bias', 'deit.layernorm.bias'),
              ('head.weight', 'cls_classifier.weight'),
              ('head.bias', 'cls_classifier.bias'),
              ('head_dist.weight', 'distillation_classifier.weight'),
              ('head_dist.bias', 'distillation_classifier.bias'),
          ]						)

   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   for i in range(config.num_hidden_layers						):
      if base_model:
         A__       =     ''
      else:
         A__       =     'deit.'
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""						)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""						)
      # next, add query, keys and values (in that order) to the state dict
      A__       =     in_proj_weight[
          : config.hidden_size, :
      ]
      A__       =     in_proj_bias[: config.hidden_size]
      A__       =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      A__       =     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      A__       =     in_proj_weight[
          -config.hidden_size :, :
      ]
      A__       =     in_proj_bias[-config.hidden_size :]







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						)
   return im







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     DeiTConfig()
   # all deit models have fine-tuned heads
   A__       =     False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   A__       =     1000
   A__       =     'huggingface/label-files'
   A__       =     'imagenet-1k-id2label.json'
   A__       =     json.load(open(hf_hub_download(UpperCAmelCase      ,UpperCAmelCase      ,repo_type='dataset'						)      ,'r'						)						)
   A__       =     {int(UpperCAmelCase						): v for k, v in idalabel.items()}
   A__       =     idalabel
   A__       =     {v: k for k, v in idalabel.items()}
   A__       =     int(deit_name[-6:-4]						)
   A__       =     int(deit_name[-3:]						)
   # size of the architecture
   if deit_name[9:].startswith('tiny'						):
      A__       =     192
      A__       =     768
      A__       =     12
      A__       =     3
   elif deit_name[9:].startswith('small'						):
      A__       =     384
      A__       =     1536
      A__       =     12
      A__       =     6
   if deit_name[9:].startswith('base'						):
      pass
   elif deit_name[4:].startswith('large'						):
      A__       =     1024
      A__       =     4096
      A__       =     24
      A__       =     16

   # load original model from timm
   A__       =     timm.create_model(UpperCAmelCase      ,pretrained=UpperCAmelCase						)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   A__       =     timm_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase      ,UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
   read_in_q_k_v(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # load HuggingFace model
   A__       =     DeiTForImageClassificationWithTeacher(UpperCAmelCase						).eval()
   model.load_state_dict(UpperCAmelCase						)

   # Check outputs on an image, prepared by DeiTImageProcessor
   A__       =     int(
       (256 / 224) * config.image_size						)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
   A__       =     DeiTImageProcessor(size=UpperCAmelCase      ,crop_size=config.image_size						)
   A__       =     image_processor(images=prepare_img()      ,return_tensors='pt'						)
   A__       =     encoding['pixel_values']
   A__       =     model(UpperCAmelCase						)

   A__       =     timm_model(UpperCAmelCase						)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(UpperCAmelCase      ,outputs.logits      ,atol=1e-3						)

   Path(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
   print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""						)
   model.save_pretrained(UpperCAmelCase						)
   print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
   image_processor.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--deit_name''',
        default='''vit_deit_base_distilled_patch16_224''',
        type=str,
        help='''Name of the DeiT timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    lowerCAmelCase_     						=				parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


